A problem of finding a pattern which maximizes or minimizes an amount matching an object (called an objective function) from many combinatorial patterns appears in various scenes of the society. A problem like this is called a combinatorial optimization problem. In this combinatorial optimization problem, the number of solution candidates exponentially increases with respect to the size of the problem. Therefore, it is in many cases difficult to solve combinatorial optimization problems by using present von Neumann computers which perform sequential computation.
A combinatorial optimization problem which maximizes or minimizes a quadratic function including a variable taking a bit value is called a QUBO (Quadratic Unconstrained Binary Optimization) problem.
Many combinatorial optimization problems can be formulated into QUBO problems. It is known to be difficult to solve QUBO problems in many cases. When a bit value is associated with an Ising spin ±1 as a classical spin model, the QUBO problem comes down to a problem (called an Ising problem) for obtaining the minimum energy state (ground state) of an Ising model of statistical mechanics.
Recently, computation apparatuses reportedly capable of rapidly solving this QUBO problem (Ising problem) are attracting attention. For example, a method of finding the ground state of the Ising model by using an oscillation phenomenon of a laser network or parametric oscillator network has been proposed. This method using the oscillation phenomenon is a method which is new in principle and different from simulated annealing (using thermal fluctuation) and quantum annealing (using quantum fluctuation) as earlier QUBO problem solution methods, and the method is presumably hardly trapped to a local optimal solution during computation.
In this method using the oscillation phenomenon, however, a state actually obtained by an oscillation threshold sometimes becomes an approximate solution. To reach a true optimal solution in this case, it is necessary to find better solutions while sneaking away from local optimal solutions by some noise as in simulated annealing. According to the known principle, a loss is indispensable in principle, and a loss destroys a quantum-mechanical superposition state, so a quantum-mechanical effect such as quantum annealing cannot be expected. As a consequence, the accuracy of a solution cannot largely be increased.